Overview

Dataset statistics

Number of variables20
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory162.2 B

Variable types

NUM17
CAT2
BOOL1

Warnings

T(C) has constant value "57" Constant
nPortlandite has constant value "57" Constant
Vol(aq) is highly correlated with b(H2O) and 2 other fieldsHigh correlation
b(H2O) is highly correlated with Vol(aq) and 2 other fieldsHigh correlation
nCa(aq) is highly correlated with b(H2O) and 2 other fieldsHigh correlation
nCa(s) is highly correlated with b(CaO) and 5 other fieldsHigh correlation
b(CaO) is highly correlated with nCa(s) and 5 other fieldsHigh correlation
nSi(aq) is highly correlated with b(H2O) and 2 other fieldsHigh correlation
nSi(s_reac) is highly correlated with b(SiO2)High correlation
b(SiO2) is highly correlated with nSi(s_reac)High correlation
mCSHQ is highly correlated with b(CaO) and 5 other fieldsHigh correlation
nCa(CSHQ) is highly correlated with b(CaO) and 5 other fieldsHigh correlation
nSi(CSHQ) is highly correlated with b(CaO) and 5 other fieldsHigh correlation
nH2O(CSHQ) is highly correlated with b(CaO) and 5 other fieldsHigh correlation
nGelPW(CSH) is highly correlated with b(CaO) and 5 other fieldsHigh correlation
ratio is highly correlated with nAmor-SlHigh correlation
nAmor-Sl is highly correlated with ratioHigh correlation
df_index has unique values Unique
b(CaO) has unique values Unique
b(SiO2) has unique values Unique
b(H2O) has unique values Unique
Vol(aq) has unique values Unique
nCa(aq) has unique values Unique
nCa(s) has unique values Unique
nSi(aq) has unique values Unique
nSi(s_reac) has unique values Unique
nAmor-Sl has unique values Unique
mCSHQ has unique values Unique
nCa(CSHQ) has unique values Unique
nSi(CSHQ) has unique values Unique
nH2O(CSHQ) has unique values Unique
nGelPW(CSH) has unique values Unique
ratio has unique values Unique

Reproduction

Analysis started2022-10-27 18:07:57.676450
Analysis finished2022-10-27 18:09:07.210703
Duration1 minute and 9.53 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.0526316
Minimum31
Maximum496
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:07.545486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile50.4
Q1163
median252
Q3351
95-th percentile477.4
Maximum496
Range465
Interquartile range (IQR)188

Descriptive statistics

Standard deviation133.3488204
Coefficient of variation (CV)0.5248865936
Kurtosis-0.8920915935
Mean254.0526316
Median Absolute Deviation (MAD)95
Skewness0.09084583352
Sum14481
Variance17781.90789
MonotocityStrictly increasing
2022-10-27T13:09:08.467364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3111.8%
 
25811.8%
 
26511.8%
 
26911.8%
 
28211.8%
 
28611.8%
 
29111.8%
 
29611.8%
 
29911.8%
 
31211.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
3111.8%
 
3711.8%
 
4011.8%
 
5311.8%
 
5411.8%
 
ValueCountFrequency (%) 
49611.8%
 
49511.8%
 
48311.8%
 
47611.8%
 
46211.8%
 

T(C)
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size456.0 B
25
57 
ValueCountFrequency (%) 
2557100.0%
 
2022-10-27T13:09:08.920494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-27T13:09:09.107996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:09.292123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

b(CaO)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2244544228
Minimum0.1028776
Maximum0.4556456
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:09.773237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1028776
5-th percentile0.11027026
Q10.1489311
median0.2044253
Q30.2697987
95-th percentile0.39141694
Maximum0.4556456
Range0.352768
Interquartile range (IQR)0.1208676

Descriptive statistics

Standard deviation0.09162165182
Coefficient of variation (CV)0.4081971327
Kurtosis-0.220475723
Mean0.2244544228
Median Absolute Deviation (MAD)0.060285
Skewness0.7238861373
Sum12.7939021
Variance0.008394527083
MonotocityNot monotonic
2022-10-27T13:09:10.132619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.152724411.8%
 
0.139754811.8%
 
0.123860211.8%
 
0.354803611.8%
 
0.455645611.8%
 
0.387590211.8%
 
0.255004611.8%
 
0.236663311.8%
 
0.251942511.8%
 
0.17152211.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.102877611.8%
 
0.105322411.8%
 
0.107835711.8%
 
0.110878911.8%
 
0.114970311.8%
 
ValueCountFrequency (%) 
0.455645611.8%
 
0.445291711.8%
 
0.406723911.8%
 
0.387590211.8%
 
0.367128211.8%
 

b(SiO2)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5431829386
Minimum0.2279184
Maximum0.697487
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:10.460752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2279184
5-th percentile0.31814978
Q10.4592235
median0.549746
Q30.6555587
95-th percentile0.68958462
Maximum0.697487
Range0.4695686
Interquartile range (IQR)0.1963352

Descriptive statistics

Standard deviation0.1189859238
Coefficient of variation (CV)0.2190531317
Kurtosis-0.3343408567
Mean0.5431829386
Median Absolute Deviation (MAD)0.0926767
Skewness-0.6551795265
Sum30.9614275
Variance0.01415765005
MonotocityNot monotonic
2022-10-27T13:09:10.890342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.693348511.8%
 
0.319187311.8%
 
0.369198411.8%
 
0.626308511.8%
 
0.686210211.8%
 
0.591291111.8%
 
0.661719511.8%
 
0.51122211.8%
 
0.655558711.8%
 
0.551393411.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.227918411.8%
 
0.295495111.8%
 
0.313999711.8%
 
0.319187311.8%
 
0.343876611.8%
 
ValueCountFrequency (%) 
0.69748711.8%
 
0.693348511.8%
 
0.691391911.8%
 
0.689132811.8%
 
0.686210211.8%
 

b(H2O)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.424318877
Minimum2.803312
Maximum8.323656
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:11.320294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.803312
5-th percentile3.075391
Q13.764618
median5.455818
Q36.698264
95-th percentile8.1333922
Maximum8.323656
Range5.520344
Interquartile range (IQR)2.933646

Descriptive statistics

Standard deviation1.715880169
Coefficient of variation (CV)0.3163309916
Kurtosis-1.303273157
Mean5.424318877
Median Absolute Deviation (MAD)1.561547
Skewness0.07857094717
Sum309.186176
Variance2.944244754
MonotocityNot monotonic
2022-10-27T13:09:11.633562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.04154311.8%
 
3.30079711.8%
 
4.65603511.8%
 
6.06341411.8%
 
6.58076411.8%
 
6.76041111.8%
 
3.6289711.8%
 
8.21836211.8%
 
3.08972211.8%
 
4.37307911.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
2.80331211.8%
 
2.87288311.8%
 
3.01806711.8%
 
3.08972211.8%
 
3.2292711.8%
 
ValueCountFrequency (%) 
8.32365611.8%
 
8.21836211.8%
 
8.15935711.8%
 
8.12690111.8%
 
8.09042111.8%
 

Vol(aq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08889919158
Minimum0.03801907
Maximum0.1457335
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:11.946074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.03801907
5-th percentile0.045087364
Q10.05857635
median0.09074376
Q30.1104424
95-th percentile0.13744296
Maximum0.1457335
Range0.10771443
Interquartile range (IQR)0.05186605

Descriptive statistics

Standard deviation0.03094395801
Coefficient of variation (CV)0.3480791834
Kurtosis-1.197331061
Mean0.08889919158
Median Absolute Deviation (MAD)0.02819574
Skewness0.0573922181
Sum5.06725392
Variance0.0009575285371
MonotocityNot monotonic
2022-10-27T13:09:12.274188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0848953411.8%
 
0.0539680711.8%
 
0.0791015411.8%
 
0.095154311.8%
 
0.100407711.8%
 
0.106417211.8%
 
0.0552181711.8%
 
0.138889611.8%
 
0.0455987311.8%
 
0.0720534411.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.0380190711.8%
 
0.0441848311.8%
 
0.0446728211.8%
 
0.04519111.8%
 
0.0455987311.8%
 
ValueCountFrequency (%) 
0.145733511.8%
 
0.141580311.8%
 
0.138889611.8%
 
0.137081311.8%
 
0.133606911.8%
 

pH
Real number (ℝ≥0)

Distinct8
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.793655404
Minimum9.793652
Maximum9.793659
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:12.565450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9.793652
5-th percentile9.793653
Q19.793655
median9.793655
Q39.793657
95-th percentile9.793658
Maximum9.793659
Range7e-06
Interquartile range (IQR)2e-06

Descriptive statistics

Standard deviation1.556778371e-06
Coefficient of variation (CV)1.589578464e-07
Kurtosis0
Mean9.793655404
Median Absolute Deviation (MAD)9.999999993e-07
Skewness0
Sum558.238358
Variance2.423558897e-12
MonotocityNot monotonic
2022-10-27T13:09:12.758437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
9.7936552747.4%
 
9.793657915.8%
 
9.793653610.5%
 
9.79365447.0%
 
9.79365647.0%
 
9.79365847.0%
 
9.79365923.5%
 
9.79365211.8%
 
ValueCountFrequency (%) 
9.79365211.8%
 
9.793653610.5%
 
9.79365447.0%
 
9.7936552747.4%
 
9.79365647.0%
 
ValueCountFrequency (%) 
9.79365923.5%
 
9.79365847.0%
 
9.793657915.8%
 
9.79365647.0%
 
9.7936552747.4%
 

nCa(aq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.329357351e-05
Minimum3.989865e-05
Maximum0.0001529326
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:13.071512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.989865e-05
5-th percentile4.731039e-05
Q16.147421e-05
median9.522724e-05
Q30.0001159328
95-th percentile0.00014425188
Maximum0.0001529326
Range0.00011303395
Interquartile range (IQR)5.445859e-05

Descriptive statistics

Standard deviation3.247371341e-05
Coefficient of variation (CV)0.3480809255
Kurtosis-1.197511464
Mean9.329357351e-05
Median Absolute Deviation (MAD)2.958776e-05
Skewness0.05728131857
Sum0.00531773369
Variance1.054542063e-09
MonotocityNot monotonic
2022-10-27T13:09:14.291085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.90889e-0511.8%
 
5.663391e-0511.8%
 
8.298926e-0511.8%
 
9.985627e-0511.8%
 
0.000105370111.8%
 
0.00011167411.8%
 
5.793449e-0511.8%
 
0.00014573711.8%
 
4.785045e-0511.8%
 
7.561329e-0511.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
3.989865e-0511.8%
 
4.636751e-0511.8%
 
4.687967e-0511.8%
 
4.741807e-0511.8%
 
4.785045e-0511.8%
 
ValueCountFrequency (%) 
0.000152932611.8%
 
0.000148574211.8%
 
0.00014573711.8%
 
0.000143880611.8%
 
0.000140203111.8%
 

nCa(s)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2243611333
Minimum0.1028196
Maximum0.4555402
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:14.619943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1028196
5-th percentile0.11016906
Q10.1488636
median0.2043117
Q30.2697194
95-th percentile0.3913196
Maximum0.4555402
Range0.3527206
Interquartile range (IQR)0.1208558

Descriptive statistics

Standard deviation0.0916230754
Coefficient of variation (CV)0.4083732063
Kurtosis-0.2205601647
Mean0.2243611333
Median Absolute Deviation (MAD)0.06032
Skewness0.72381777
Sum12.7885846
Variance0.008394787945
MonotocityNot monotonic
2022-10-27T13:09:14.948066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.152635311.8%
 
0.139698211.8%
 
0.123777211.8%
 
0.354703711.8%
 
0.455540211.8%
 
0.387478511.8%
 
0.254946711.8%
 
0.236517611.8%
 
0.251894611.8%
 
0.171446411.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.102819611.8%
 
0.105193711.8%
 
0.107753311.8%
 
0.11077311.8%
 
0.114817411.8%
 
ValueCountFrequency (%) 
0.455540211.8%
 
0.44518711.8%
 
0.40668411.8%
 
0.387478511.8%
 
0.367023811.8%
 

nSi(aq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0003502914772
Minimum0.0001498063
Maximum0.0005742409
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:15.260562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0001498063
5-th percentile0.00017764936
Q10.0002308084
median0.0003575389
Q30.0004352515
95-th percentile0.00054162148
Maximum0.0005742409
Range0.0004244346
Interquartile range (IQR)0.0002044431

Descriptive statistics

Standard deviation0.0001219285541
Coefficient of variation (CV)0.3480774213
Kurtosis-1.197359756
Mean0.0003502914772
Median Absolute Deviation (MAD)0.0001110944
Skewness0.05734063547
Sum0.0199666142
Variance1.48665723e-08
MonotocityNot monotonic
2022-10-27T13:09:15.582664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.000334514511.8%
 
0.000212656811.8%
 
0.000311641111.8%
 
0.00037493511.8%
 
0.000395594411.8%
 
0.000419330211.8%
 
0.000217553811.8%
 
0.000547233411.8%
 
0.000179669511.8%
 
0.000283914511.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.000149806311.8%
 
0.000174093811.8%
 
0.000176020411.8%
 
0.000178056611.8%
 
0.000179669511.8%
 
ValueCountFrequency (%) 
0.000574240911.8%
 
0.000557865911.8%
 
0.000547233411.8%
 
0.000540218511.8%
 
0.000526399311.8%
 

nSi(s_reac)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5428326491
Minimum0.2275989
Maximum0.6970517
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:15.869992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2275989
5-th percentile0.31789022
Q10.45897
median0.5492242
Q30.655379
95-th percentile0.68915518
Maximum0.6970517
Range0.4694528
Interquartile range (IQR)0.196409

Descriptive statistics

Standard deviation0.1189676879
Coefficient of variation (CV)0.2191608926
Kurtosis-0.334730621
Mean0.5428326491
Median Absolute Deviation (MAD)0.0930151
Skewness-0.6545423293
Sum30.941461
Variance0.01415331076
MonotocityNot monotonic
2022-10-27T13:09:16.182495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.69301411.8%
 
0.318974611.8%
 
0.368886811.8%
 
0.625933611.8%
 
0.685814611.8%
 
0.590871811.8%
 
0.661501911.8%
 
0.510674811.8%
 
0.65537911.8%
 
0.551109511.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.227598911.8%
 
0.295252411.8%
 
0.313552711.8%
 
0.318974611.8%
 
0.343700611.8%
 
ValueCountFrequency (%) 
0.697051711.8%
 
0.69301411.8%
 
0.691177511.8%
 
0.688649611.8%
 
0.685814611.8%
 

nPortlandite
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size456.0 B
0
57 
ValueCountFrequency (%) 
057100.0%
 
2022-10-27T13:09:16.385636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

nAmor-Sl
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2103951929
Minimum0.009095025
Maximum0.5327833
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:16.568196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.009095025
5-th percentile0.015825062
Q10.09634542
median0.1956904
Q30.3085267
95-th percentile0.44932056
Maximum0.5327833
Range0.523688275
Interquartile range (IQR)0.21218128

Descriptive statistics

Standard deviation0.1393682061
Coefficient of variation (CV)0.662411551
Kurtosis-0.7785171742
Mean0.2103951929
Median Absolute Deviation (MAD)0.11198938
Skewness0.3899102712
Sum11.99252599
Variance0.01942349686
MonotocityNot monotonic
2022-10-27T13:09:16.884219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.466853211.8%
 
0.111982911.8%
 
0.185485211.8%
 
0.100366511.8%
 
0.0108374211.8%
 
0.0167421511.8%
 
0.283745711.8%
 
0.160225111.8%
 
0.28214511.8%
 
0.297076211.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.00909502511.8%
 
0.0108374211.8%
 
0.0121567111.8%
 
0.0167421511.8%
 
0.0171969111.8%
 
ValueCountFrequency (%) 
0.532783311.8%
 
0.500443311.8%
 
0.466853211.8%
 
0.444937411.8%
 
0.433533211.8%
 

mCSHQ
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04163886947
Minimum0.01908214
Maximum0.08454308
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:17.196727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.01908214
5-th percentile0.020446118
Q10.02762739
median0.03791793
Q30.05005684
95-th percentile0.072624464
Maximum0.08454308
Range0.06546094
Interquartile range (IQR)0.02242945

Descriptive statistics

Standard deviation0.01700420016
Coefficient of variation (CV)0.4083732431
Kurtosis-0.2205594367
Mean0.04163886947
Median Absolute Deviation (MAD)0.01119471
Skewness0.7238179295
Sum2.37341556
Variance0.0002891428232
MonotocityNot monotonic
2022-10-27T13:09:17.536298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0283273711.8%
 
0.0259263911.8%
 
0.0229716411.8%
 
0.0658289711.8%
 
0.0845430811.8%
 
0.071911611.8%
 
0.047315211.8%
 
0.0438949611.8%
 
0.0467487811.8%
 
0.031818511.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.0190821411.8%
 
0.0195227511.8%
 
0.0199977911.8%
 
0.020558211.8%
 
0.0213087911.8%
 
ValueCountFrequency (%) 
0.0845430811.8%
 
0.0826216411.8%
 
0.0754759211.8%
 
0.071911611.8%
 
0.0681154411.8%
 

nCa(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2243611333
Minimum0.1028196
Maximum0.4555402
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:17.869709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1028196
5-th percentile0.11016906
Q10.1488636
median0.2043117
Q30.2697194
95-th percentile0.3913196
Maximum0.4555402
Range0.3527206
Interquartile range (IQR)0.1208558

Descriptive statistics

Standard deviation0.0916230754
Coefficient of variation (CV)0.4083732063
Kurtosis-0.2205601647
Mean0.2243611333
Median Absolute Deviation (MAD)0.06032
Skewness0.72381777
Sum12.7885846
Variance0.008394787945
MonotocityNot monotonic
2022-10-27T13:09:18.197821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.152635311.8%
 
0.139698211.8%
 
0.123777211.8%
 
0.354703711.8%
 
0.455540211.8%
 
0.387478511.8%
 
0.254946711.8%
 
0.236517611.8%
 
0.251894611.8%
 
0.171446411.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.102819611.8%
 
0.105193711.8%
 
0.107753311.8%
 
0.11077311.8%
 
0.114817411.8%
 
ValueCountFrequency (%) 
0.455540211.8%
 
0.44518711.8%
 
0.40668411.8%
 
0.387478511.8%
 
0.367023811.8%
 

nSi(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3324374544
Minimum0.1523485
Maximum0.6749772
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:18.528388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1523485
5-th percentile0.16323826
Q10.2205722
median0.3027301
Q30.3996451
95-th percentile0.57982098
Maximum0.6749772
Range0.5226287
Interquartile range (IQR)0.1790729

Descriptive statistics

Standard deviation0.1357585556
Coefficient of variation (CV)0.4083732257
Kurtosis-0.2205593059
Mean0.3324374544
Median Absolute Deviation (MAD)0.0893766
Skewness0.7238179667
Sum18.9489349
Variance0.01843038541
MonotocityNot monotonic
2022-10-27T13:09:18.869872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.226160811.8%
 
0.206991711.8%
 
0.183401611.8%
 
0.52556711.8%
 
0.674977211.8%
 
0.574129611.8%
 
0.377756311.8%
 
0.350449711.8%
 
0.373234111.8%
 
0.254033311.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.152348511.8%
 
0.155866211.8%
 
0.159658911.8%
 
0.164133111.8%
 
0.170125711.8%
 
ValueCountFrequency (%) 
0.674977211.8%
 
0.659636711.8%
 
0.602586511.8%
 
0.574129611.8%
 
0.543821811.8%
 

nH2O(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5041830474
Minimum0.2310555
Maximum1.023687
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:19.443361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2310555
5-th percentile0.24757124
Q10.3345254
median0.4591282
Q30.6061119
95-th percentile0.8793712
Maximum1.023687
Range0.7926315
Interquartile range (IQR)0.2715865

Descriptive statistics

Standard deviation0.2058948628
Coefficient of variation (CV)0.4083732365
Kurtosis-0.2205604511
Mean0.5041830474
Median Absolute Deviation (MAD)0.1355508
Skewness0.723817638
Sum28.7384337
Variance0.04239269454
MonotocityNot monotonic
2022-10-27T13:09:19.810429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.343001211.8%
 
0.313928911.8%
 
0.278151411.8%
 
0.797088211.8%
 
1.02368711.8%
 
0.870739511.8%
 
0.572914711.8%
 
0.531500911.8%
 
0.566056211.8%
 
0.385273311.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.231055511.8%
 
0.236390711.8%
 
0.242142611.8%
 
0.248928411.8%
 
0.258016911.8%
 
ValueCountFrequency (%) 
1.02368711.8%
 
1.00042211.8%
 
0.91389811.8%
 
0.870739511.8%
 
0.824773911.8%
 

C/S(CSHQ)
Categorical

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size456.0 B
0.6748973
27 
0.6748972
26 
0.6748974
0.6748971
 
1
ValueCountFrequency (%) 
0.67489732747.4%
 
0.67489722645.6%
 
0.674897435.3%
 
0.674897111.8%
 
2022-10-27T13:09:20.213637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)1.8%
2022-10-27T13:09:20.400644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:20.586660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length9
Min length9

nGelPW(CSH)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1722607321
Minimum0.07894314
Maximum0.3497562
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:20.842033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.07894314
5-th percentile0.084585972
Q10.114295
median0.1568672
Q30.207086
95-th percentile0.30044864
Maximum0.3497562
Range0.27081306
Interquartile range (IQR)0.092791

Descriptive statistics

Standard deviation0.07034667657
Coefficient of variation (CV)0.408373259
Kurtosis-0.220559514
Mean0.1722607321
Median Absolute Deviation (MAD)0.0463128
Skewness0.7238179714
Sum9.81886173
Variance0.004948654905
MonotocityNot monotonic
2022-10-27T13:09:21.247620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.117190911.8%
 
0.107257911.8%
 
0.0950340711.8%
 
0.272335611.8%
 
0.349756211.8%
 
0.297499511.8%
 
0.195743811.8%
 
0.181594211.8%
 
0.193400511.8%
 
0.131633711.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.0789431411.8%
 
0.0807659711.8%
 
0.0827311811.8%
 
0.0850496711.8%
 
0.0881548711.8%
 
ValueCountFrequency (%) 
0.349756211.8%
 
0.341807211.8%
 
0.312245211.8%
 
0.297499511.8%
 
0.281794811.8%
 

ratio
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4205797239
Minimum0.152833242
Maximum0.6654837782
Zeros0
Zeros (%)0.0%
Memory size456.0 B
2022-10-27T13:09:21.595726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.152833242
5-th percentile0.1893608906
Q10.3078932816
median0.4105637726
Q30.5522682849
95-th percentile0.6466042996
Maximum0.6654837782
Range0.5126505362
Interquartile range (IQR)0.2443750032

Descriptive statistics

Standard deviation0.1474662254
Coefficient of variation (CV)0.350626093
Kurtosis-1.129619332
Mean0.4205797239
Median Absolute Deviation (MAD)0.1270488804
Skewness-0.04900552407
Sum23.97304426
Variance0.02174628762
MonotocityNot monotonic
2022-10-27T13:09:21.941734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.220270758511.8%
 
0.437845741411.8%
 
0.335484119111.8%
 
0.566499736211.8%
 
0.664002954211.8%
 
0.655498112511.8%
 
0.385366609311.8%
 
0.462936454211.8%
 
0.384317224411.8%
 
0.311070099911.8%
 
Other values (47)4782.5%
 
ValueCountFrequency (%) 
0.15283324211.8%
 
0.163285618111.8%
 
0.186750055611.8%
 
0.190013599411.8%
 
0.198164179311.8%
 
ValueCountFrequency (%) 
0.665483778211.8%
 
0.664002954211.8%
 
0.655498112511.8%
 
0.644380846311.8%
 
0.638326699411.8%
 

Interactions

2022-10-27T13:07:59.517683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:07:59.694937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:07:59.898061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:00.085562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:00.273068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:00.491820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:00.694545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:00.882047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:01.085174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:01.288301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:01.475802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:01.663692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:01.882443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:02.085572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:02.288697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:02.507455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:02.756349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:02.946100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:03.180475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:03.430480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:03.664571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:03.867695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:05.758348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:06.023967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:06.242721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:06.477106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:06.711780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:06.930542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:07.149289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:07.399315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:07.664649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:07.903396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:08.122141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:08.356609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:08.586691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:08.774196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:08.992960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:09.180466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:09.383580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:09.619148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:09.869140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:10.089633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:10.292775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:10.464644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:10.683351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:10.886481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:11.089605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:11.297210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:11.484713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:11.698853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:11.886372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:12.073868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:12.261363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:12.464483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:12.650779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:12.887555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:13.153182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:13.387563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:13.590481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:13.809245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:14.012379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:14.199891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:14.387380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:14.605042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:14.823786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:15.026924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:15.245677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:15.464413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:15.668720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:15.871851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:16.090592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:16.309356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:16.541307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:16.762468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:16.965596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:17.713783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:18.035791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:18.238925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:18.488939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:18.714215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:18.948578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:19.167327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:19.386071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:19.635816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:19.854553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:08:20.073294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-10-27T13:09:04.482338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:04.668458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:04.871577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:05.090330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:05.277829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:05.465328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-10-27T13:09:22.315921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-27T13:09:23.082916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-27T13:09:23.733340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-27T13:09:24.423508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-27T13:09:05.998176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:09:06.857748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

df_indexT(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)ratio
03125.00.1527240.6933495.0415430.0848959.7936550.0000890.1526350.0003350.6930140.00.4668530.0283270.1526350.2261610.3430010.6748970.1171910.220271
13725.00.1345310.5028422.8033120.0451919.7936540.0000470.1344840.0001780.5026640.00.3033980.0249590.1344840.1992660.3022120.6748970.1032550.267542
24025.00.1773820.4587114.8506500.0804459.7936570.0000840.1772980.0003170.4583940.00.1956900.0329040.1772980.2627030.3984220.6748970.1361260.386697
35325.00.3581620.6424233.3804830.0465399.7936560.0000490.3581130.0001830.6422390.00.1116210.0664620.3581130.5306180.8047490.6748970.2749530.557517
45425.00.1149700.6156378.3236560.1457339.7936550.0001530.1148170.0005740.6150630.00.4449370.0213090.1148170.1701260.2580170.6748970.0881550.186750
55925.00.2574330.6100993.3738600.0505109.7936580.0000530.2573800.0001990.6099000.00.2285390.0477670.2573800.3813620.5783830.6748970.1976120.421953
66225.00.3163000.5167627.6254440.1249429.7936550.0001310.3161690.0004920.5162690.00.0477990.0586770.3161690.4684700.7104940.6748970.2427500.612082
77125.00.2044250.4983466.4479760.1082099.7936550.0001140.2043120.0004260.4979190.00.1951890.0379180.2043120.3027300.4591280.6748970.1568670.410208
88725.00.1582950.2954953.7646180.0615969.7936530.0000650.1582310.0002430.2952520.00.0608010.0293660.1582310.2344510.3555750.6748970.1214870.535695
99725.00.2145080.6651887.4092220.1251699.7936550.0001310.2143760.0004930.6646940.00.3470520.0397860.2143760.3176430.4817450.6748970.1645950.322477

Last rows

df_indexT(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)ratio
4740225.00.2653060.5392733.8379700.0585769.7936560.0000610.2652450.0002310.5390420.00.1460280.0492260.2652450.3930150.5960560.6748970.2036500.491970
4840525.00.2442780.6718015.6477060.0921319.7936550.0000970.2441820.0003630.6714380.00.3096320.0453170.2441820.3618060.5487240.6748970.1874790.363617
4942825.00.4452920.6691256.5213590.0997559.7936550.0001050.4451870.0003930.6687320.00.0090950.0826220.4451870.6596371.0004220.6748970.3418070.665484
5044825.00.1454860.2279184.8145160.0810879.7936530.0000850.1454010.0003200.2275990.00.0121570.0269850.1454010.2154420.3267450.6748970.1116370.638327
5145025.00.1108790.4522845.8341200.1009169.7936550.0001060.1107730.0003980.4518860.00.2877530.0205580.1107730.1641330.2489280.6748970.0850500.245153
5246225.00.2467950.3829953.3568800.0506359.7936530.0000530.2467420.0001990.3827960.00.0171970.0457920.2467420.3655990.5544770.6748970.1894440.644381
5347625.00.1053220.6891337.0237930.1226389.7936550.0001290.1051940.0004830.6886500.00.5327830.0195230.1051940.1558660.2363910.6748970.0807660.152833
5448325.00.1441400.5824978.1593570.1415809.7936550.0001490.1439920.0005580.5819390.00.3685860.0267230.1439920.2133540.3235770.6748970.1105540.247452
5549525.00.2312430.4391043.4117360.0522589.7936570.0000550.2311880.0002060.4388980.00.0963450.0429060.2311880.3425530.5195240.6748970.1775020.526624
5649625.00.1782520.3438772.8728830.0446739.7936550.0000470.1782050.0001760.3437010.00.0796530.0330730.1782050.2640480.4004620.6748970.1368230.518360